Implications of within-farm transmission for network dynamics: Consequences for the spread of avian influenza

Sema Nickbakhsh*, Louise Matthews, Jennifer E. Dent, Giles T Innocent, Mark E. Arnold, Stuart W J Reid, Rowland R. Kao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The importance of considering coupled interactions across multiple population scales has not previously been studied for highly pathogenic avian influenza (HPAI) in the British commercial poultry industry. By simulating the within-flock transmission of HPAI using a deterministic S-E-I-R model, and by incorporating an additional environmental class representing infectious faeces, we tracked the build-up of infectious faeces within a poultry house over time. A measure of the transmission risk (TR) was computed for each farm by linking the amount of infectious faeces present each day of an outbreak with data describing the daily on-farm visit schedules for a major British catching company. Larger flocks tended to have greater levels of these catching-team visits. However, where density-dependent contact was assumed, faster outbreak detection (according to an assumed mortality threshold) led to a decreased opportunity for catching-team visits to coincide with an outbreak. For this reason, maximum TR-levels were found for mid-range flock sizes (similar to 25,000-35,000 birds). When assessing all factors simultaneously using multi-variable linear regression on the simulated outputs, those related to the pattern of catching-team visits had the largest effect on TR, with the most important movement-related factor depending on the mode of transmission. Using social network analysis on a further database to inform a measure of between-farm connectivity, we identified a large fraction of farms (28%) that had both a high TR and a high potential impact at the between farm level. Our results have counter-intuitive implications for between-farm spread that could not be predicted based on flock size alone, and together with further knowledge of the relative importance of transmission risk and impact, could have implications for improved targeting of control measures. (C) 2013 Elsevier B. V. All rights reserved.

Original languageEnglish
Pages (from-to)67-76
Number of pages10
JournalEpidemics
Volume5
Issue number2
Early online date15 Mar 2013
DOIs
Publication statusPublished - Jun 2013

Keywords

  • Mathematical modelling
  • Social network data
  • Poultry
  • GREAT-BRITAIN
  • LIVESTOCK MOVEMENTS
  • REVERSE GENETICS
  • POULTRY-INDUSTRY
  • POTENTIAL SPREAD
  • VIRUS EPIDEMIC
  • H5N1
  • CHICKENS
  • FLOCK
  • MODEL

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